{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第8章 方差分析"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 本章需要用到的库\n",
    "import numpy as np # 导入numpy库\n",
    "import pandas as pd # 导入pandas库\n",
    "import matplotlib.pyplot as plt # 导入matplotlib库\n",
    "import seaborn as sns # 导入seaborn库\n",
    "from scipy.stats import shapiro, kstest # 导入正态性检验函数: S-W检验和K-S检验\n",
    "from scipy.stats import bartlett, levene # 导入方差齐性检验函数: Bartlett检验和Levene检验\n",
    "import statsmodels.api as sm # 导入statsmodels库\n",
    "from statsmodels.formula.api import ols # 导入普通最小二乘法函数\n",
    "from statsmodels.stats.anova import anova_lm # 导入方差分析函数\n",
    "from statsmodels.stats.multicomp import MultiComparison # 导入多重比较函数\n",
    "from statsmodels.graphics.api import interaction_plot # 导入交互作用图函数\n",
    "\n",
    "# 设置初始化\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.1 方差分析的原理"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.1.1 什么是方差分析"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.1.2 误差分解"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.2 单因子方差分析"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.2.1 数学模型"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.2.2 效应检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>品种1</th>\n",
       "      <th>品种2</th>\n",
       "      <th>品种3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81</td>\n",
       "      <td>71</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82</td>\n",
       "      <td>72</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>79</td>\n",
       "      <td>72</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>81</td>\n",
       "      <td>66</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78</td>\n",
       "      <td>72</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>89</td>\n",
       "      <td>77</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>92</td>\n",
       "      <td>81</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>87</td>\n",
       "      <td>77</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>85</td>\n",
       "      <td>73</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>86</td>\n",
       "      <td>79</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   品种1  品种2  品种3\n",
       "0   81   71   76\n",
       "1   82   72   79\n",
       "2   79   72   77\n",
       "3   81   66   76\n",
       "4   78   72   78\n",
       "5   89   77   89\n",
       "6   92   81   87\n",
       "7   87   77   84\n",
       "8   85   73   87\n",
       "9   86   79   87"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example8_1 = pd.read_csv('./pydata/chap08/example8_1.csv', encoding='gbk') # 读取数据\n",
    "example8_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>品种</th>\n",
       "      <th>产量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>品种1</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>品种1</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>品种1</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>品种1</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>品种1</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    品种  产量\n",
       "0  品种1  81\n",
       "1  品种1  82\n",
       "2  品种1  79\n",
       "3  品种1  81\n",
       "4  品种1  78"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example8_2 = pd.melt(\n",
    "    example8_1, value_vars=['品种1', '品种2', '品种3'], \n",
    "    var_name='品种', value_name='产量'\n",
    ") # 转换数据格式\n",
    "example8_2.to_csv('./pydata/chap08/example8_2.csv', encoding='gbk', index=False) # 保存数据\n",
    "example8_2.head() # 显示前5行数据"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提出假设：\n",
    "- $H_0: \\alpha_1 = \\alpha_2 = \\alpha_3 = 0$(品种对产量的影响不显著)\n",
    "- $H_1: \\alpha_1, \\alpha_2, \\alpha_3$ 至少有一个不等于0(品种对产量的影响显著)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>品种</th>\n",
       "      <td>2.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>12.312704</td>\n",
       "      <td>0.000158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>27.0</td>\n",
       "      <td>614.0</td>\n",
       "      <td>22.740741</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            df  sum_sq     mean_sq          F    PR(>F)\n",
       "品种         2.0   560.0  280.000000  12.312704  0.000158\n",
       "Residual  27.0   614.0   22.740741        NaN       NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example8_2 = pd.read_csv('./pydata/chap08/example8_2.csv', encoding='gbk') # 读取数据\n",
    "model = ols('产量 ~ 品种', data=example8_2).fit() # 拟合方差分析模型\n",
    "anova_lm(model, typ=1) # 执行方差分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>产量</td>        <th>  R-squared:         </th> <td>   0.477</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.438</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   12.31</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 14 May 2023</td> <th>  Prob (F-statistic):</th> <td>0.000158</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:36:42</td>     <th>  Log-Likelihood:    </th> <td> -87.850</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    30</td>      <th>  AIC:               </th> <td>   181.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    27</td>      <th>  BIC:               </th> <td>   185.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   84.0000</td> <td>    1.508</td> <td>   55.703</td> <td> 0.000</td> <td>   80.906</td> <td>   87.094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>品种[T.品种2]</th> <td>  -10.0000</td> <td>    2.133</td> <td>   -4.689</td> <td> 0.000</td> <td>  -14.376</td> <td>   -5.624</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>品种[T.品种3]</th> <td>   -2.0000</td> <td>    2.133</td> <td>   -0.938</td> <td> 0.357</td> <td>   -6.376</td> <td>    2.376</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.194</td> <th>  Durbin-Watson:     </th> <td>   1.094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.045</td> <th>  Jarque-Bera (JB):  </th> <td>   1.968</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.110</td> <th>  Prob(JB):          </th> <td>   0.374</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 1.765</td> <th>  Cond. No.          </th> <td>    3.73</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                     产量   R-squared:                       0.477\n",
       "Model:                            OLS   Adj. R-squared:                  0.438\n",
       "Method:                 Least Squares   F-statistic:                     12.31\n",
       "Date:                Sun, 14 May 2023   Prob (F-statistic):           0.000158\n",
       "Time:                        19:36:42   Log-Likelihood:                -87.850\n",
       "No. Observations:                  30   AIC:                             181.7\n",
       "Df Residuals:                      27   BIC:                             185.9\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     84.0000      1.508     55.703      0.000      80.906      87.094\n",
       "品种[T.品种2]    -10.0000      2.133     -4.689      0.000     -14.376      -5.624\n",
       "品种[T.品种3]     -2.0000      2.133     -0.938      0.357      -6.376       2.376\n",
       "==============================================================================\n",
       "Omnibus:                        6.194   Durbin-Watson:                   1.094\n",
       "Prob(Omnibus):                  0.045   Jarque-Bera (JB):                1.968\n",
       "Skew:                           0.110   Prob(JB):                        0.374\n",
       "Kurtosis:                       1.765   Cond. No.                         3.73\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary() # 显示方差分析结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P<0.05, 所以拒绝原假设。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>80.905833</td>\n",
       "      <td>87.094167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>品种[T.品种2]</th>\n",
       "      <td>-14.375813</td>\n",
       "      <td>-5.624187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>品种[T.品种3]</th>\n",
       "      <td>-6.375813</td>\n",
       "      <td>2.375813</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                   0          1\n",
       "Intercept  80.905833  87.094167\n",
       "品种[T.品种2] -14.375813  -5.624187\n",
       "品种[T.品种3]  -6.375813   2.375813"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.conf_int()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>品种1</th>\n",
       "      <th>品种2</th>\n",
       "      <th>品种3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>lci</th>\n",
       "      <td>80.905833</td>\n",
       "      <td>70.905833</td>\n",
       "      <td>78.905833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>84.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uci</th>\n",
       "      <td>87.094167</td>\n",
       "      <td>77.094167</td>\n",
       "      <td>85.094167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            品种1        品种2        品种3\n",
       "lci   80.905833  70.905833  78.905833\n",
       "mean  84.000000  74.000000  82.000000\n",
       "uci   87.094167  77.094167  85.094167"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean = model.params.values[0] # 品种1(参照的基础水平)的平均产量\n",
    "lci, uci = model.conf_int().iloc[0].values # 品种1(参照的基础水平)的置信区间\n",
    "df_mean = pd.DataFrame([lci, mean, uci], index=['lci', 'mean', 'uci'], columns=['品种1']) # 构建数据框\n",
    "df_mean['品种2'] = df_mean['品种1'] + model.params.values[1] # 品种2的平均产量\n",
    "df_mean['品种3'] = df_mean['品种1'] + model.params.values[2] # 品种3的平均产量\n",
    "df_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_mean.T.plot(\n",
    "    ls='', marker='o', legend=False, c='black', \n",
    "    figsize=(9, 6), xlabel='品种', ylabel='产量'\n",
    ") # 绘制图形\n",
    "ax = df_mean.T['mean'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 6)) # 设置图形大小\n",
    "sns.pointplot(\n",
    "    x='品种', y='产量', data=example8_2,\n",
    "    capsize=.1, errwidth=1\n",
    ")\n",
    "plt.xlabel('品种') # 添加横轴标签\n",
    "plt.ylabel('产量') # 添加纵轴标签\n",
    "plt.show() # 显示图形"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.2.3 效应量分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "效应量为 0.477\n"
     ]
    }
   ],
   "source": [
    "# 效应量分析，接上例8-2中的模型\n",
    "print(f'效应量为 {model.rsquared:.4g}') # 显示效应量"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在产量取值的总误差中被品种解释的比例为47.7%。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.2.4 多重比较"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Multiple Comparison of Means - Tukey HSD, FWER=0.05</caption>\n",
       "<tr>\n",
       "  <th>group1</th> <th>group2</th> <th>meandiff</th>  <th>p-adj</th>   <th>lower</th>   <th>upper</th>  <th>reject</th>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>品种1</td>    <td>品种2</td>    <td>-10.0</td>  <td>0.0002</td> <td>-15.2877</td> <td>-4.7123</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>品种1</td>    <td>品种3</td>    <td>-2.0</td>   <td>0.6216</td>  <td>-7.2877</td> <td>3.2877</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>品种2</td>    <td>品种3</td>     <td>8.0</td>   <td>0.0024</td>  <td>2.7123</td>  <td>13.2877</td>  <td>True</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mc = MultiComparison(example8_2['产量'], example8_2['品种']) # 创建多重比较对象\n",
    "mc.tukeyhsd(alpha=0.05).summary() # 执行多重比较"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结果显示，品种3与品种1之间的差异不显著，品种1与品种2、品种3与品种2之间的差异均显著。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.3 双因子方差分析"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.3.1 数学模型"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.3.2 主效应分析"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "检验因子A的假设：\n",
    "- $H_0: \\alpha_i = 0, (i=1,2,\\cdots,I)$ (因子A的处理效应不显著)\n",
    "- $H_1: \\alpha_i$ 至少有一个不等于0 (因子A的处理效应显著)\n",
    "\n",
    "检验因子B的假设：\n",
    "- $H_0: \\beta_j = 0, (j=1,2,\\cdots,J)$ (因子B的处理效应不显著)\n",
    "- $H_1: \\beta_j$ 至少有一个不等于0 (因子B的处理效应显著)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>施肥方式</th>\n",
       "      <th>品种1</th>\n",
       "      <th>品种2</th>\n",
       "      <th>品种3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>甲</td>\n",
       "      <td>81</td>\n",
       "      <td>71</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>甲</td>\n",
       "      <td>82</td>\n",
       "      <td>72</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>甲</td>\n",
       "      <td>79</td>\n",
       "      <td>72</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>甲</td>\n",
       "      <td>81</td>\n",
       "      <td>66</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>甲</td>\n",
       "      <td>78</td>\n",
       "      <td>72</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>乙</td>\n",
       "      <td>89</td>\n",
       "      <td>77</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>乙</td>\n",
       "      <td>92</td>\n",
       "      <td>81</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>乙</td>\n",
       "      <td>87</td>\n",
       "      <td>77</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>乙</td>\n",
       "      <td>85</td>\n",
       "      <td>73</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>乙</td>\n",
       "      <td>86</td>\n",
       "      <td>79</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  施肥方式  品种1  品种2  品种3\n",
       "0    甲   81   71   76\n",
       "1    甲   82   72   79\n",
       "2    甲   79   72   77\n",
       "3    甲   81   66   76\n",
       "4    甲   78   72   78\n",
       "5    乙   89   77   89\n",
       "6    乙   92   81   87\n",
       "7    乙   87   77   84\n",
       "8    乙   85   73   87\n",
       "9    乙   86   79   87"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table8_4 = pd.read_csv('./pydata/chap08/table8_4.csv', encoding='gbk') # 读取数据\n",
    "table8_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>施肥方式</th>\n",
       "      <th>品种</th>\n",
       "      <th>产量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>甲</td>\n",
       "      <td>品种1</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>甲</td>\n",
       "      <td>品种1</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>甲</td>\n",
       "      <td>品种1</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>甲</td>\n",
       "      <td>品种1</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>甲</td>\n",
       "      <td>品种1</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  施肥方式   品种  产量\n",
       "0    甲  品种1  81\n",
       "1    甲  品种1  82\n",
       "2    甲  品种1  79\n",
       "3    甲  品种1  81\n",
       "4    甲  品种1  78"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example8_4 = pd.melt(\n",
    "    table8_4, id_vars=['施肥方式'],\n",
    "    value_vars=['品种1', '品种2', '品种3'],\n",
    "    var_name='品种', value_name='产量'\n",
    ") # 转换数据格式\n",
    "example8_4.to_csv('./pydata/chap08/example8_4.csv', encoding='gbk', index=False) # 保存数据\n",
    "example8_4.head() # 显示前5行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>品种</th>\n",
       "      <td>2.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>54.328358</td>\n",
       "      <td>5.184290e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>施肥方式</th>\n",
       "      <td>1.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>93.134328</td>\n",
       "      <td>4.422633e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>26.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>5.153846</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            df  sum_sq     mean_sq          F        PR(>F)\n",
       "品种         2.0   560.0  280.000000  54.328358  5.184290e-10\n",
       "施肥方式       1.0   480.0  480.000000  93.134328  4.422633e-10\n",
       "Residual  26.0   134.0    5.153846        NaN           NaN"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2m = ols('产量 ~ 品种 + 施肥方式', data=example8_4).fit() # 拟合方差分析模型\n",
    "anova_lm(model2m, typ=1) # 执行方差分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>产量</td>        <th>  R-squared:         </th> <td>   0.886</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.873</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   67.26</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 14 May 2023</td> <th>  Prob (F-statistic):</th> <td>2.21e-12</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:05:35</td>     <th>  Log-Likelihood:    </th> <td> -65.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    30</td>      <th>  AIC:               </th> <td>   138.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    26</td>      <th>  BIC:               </th> <td>   143.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   88.0000</td> <td>    0.829</td> <td>  106.157</td> <td> 0.000</td> <td>   86.296</td> <td>   89.704</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>品种[T.品种2]</th> <td>  -10.0000</td> <td>    1.015</td> <td>   -9.850</td> <td> 0.000</td> <td>  -12.087</td> <td>   -7.913</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>品种[T.品种3]</th> <td>   -2.0000</td> <td>    1.015</td> <td>   -1.970</td> <td> 0.060</td> <td>   -4.087</td> <td>    0.087</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>施肥方式[T.甲]</th> <td>   -8.0000</td> <td>    0.829</td> <td>   -9.651</td> <td> 0.000</td> <td>   -9.704</td> <td>   -6.296</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.889</td> <th>  Durbin-Watson:     </th> <td>   2.269</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.641</td> <th>  Jarque-Bera (JB):  </th> <td>   0.880</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.360</td> <th>  Prob(JB):          </th> <td>   0.644</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.570</td> <th>  Cond. No.          </th> <td>    4.22</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                     产量   R-squared:                       0.886\n",
       "Model:                            OLS   Adj. R-squared:                  0.873\n",
       "Method:                 Least Squares   F-statistic:                     67.26\n",
       "Date:                Sun, 14 May 2023   Prob (F-statistic):           2.21e-12\n",
       "Time:                        20:05:35   Log-Likelihood:                -65.018\n",
       "No. Observations:                  30   AIC:                             138.0\n",
       "Df Residuals:                      26   BIC:                             143.6\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     88.0000      0.829    106.157      0.000      86.296      89.704\n",
       "品种[T.品种2]    -10.0000      1.015     -9.850      0.000     -12.087      -7.913\n",
       "品种[T.品种3]     -2.0000      1.015     -1.970      0.060      -4.087       0.087\n",
       "施肥方式[T.甲]     -8.0000      0.829     -9.651      0.000      -9.704      -6.296\n",
       "==============================================================================\n",
       "Omnibus:                        0.889   Durbin-Watson:                   2.269\n",
       "Prob(Omnibus):                  0.641   Jarque-Bera (JB):                0.880\n",
       "Skew:                          -0.360   Prob(JB):                        0.644\n",
       "Kurtosis:                       2.570   Cond. No.                         4.22\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2m.summary() # 显示方差分析结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "检验品种和施肥方式两个因子的P值均接近于0，表示两个因子对产量均有显著影响。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "      <th>eta_sq</th>\n",
       "      <th>eta_sq_part</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>品种</th>\n",
       "      <td>2.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>54.328358</td>\n",
       "      <td>5.184290e-10</td>\n",
       "      <td>0.477002</td>\n",
       "      <td>0.806916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>施肥方式</th>\n",
       "      <td>1.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>93.134328</td>\n",
       "      <td>4.422633e-10</td>\n",
       "      <td>0.408859</td>\n",
       "      <td>0.781759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>26.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>5.153846</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.114140</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            df  sum_sq     mean_sq          F        PR(>F)    eta_sq   \n",
       "品种         2.0   560.0  280.000000  54.328358  5.184290e-10  0.477002  \\\n",
       "施肥方式       1.0   480.0  480.000000  93.134328  4.422633e-10  0.408859   \n",
       "Residual  26.0   134.0    5.153846        NaN           NaN  0.114140   \n",
       "\n",
       "          eta_sq_part  \n",
       "品种           0.806916  \n",
       "施肥方式         0.781759  \n",
       "Residual          NaN  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算主效应方差分析的效应量\n",
    "df_res = anova_lm(model2m, typ=1) # 执行方差分析\n",
    "df_res['eta_sq'] = df_res['sum_sq'] / df_res['sum_sq'].sum() # 计算主效应量\n",
    "df_res['eta_sq_part'] = df_res['sum_sq'] / (df_res['sum_sq'] + df_res.loc['Residual', 'sum_sq']) # 计算偏效应量\n",
    "df_res.loc['Residual', 'eta_sq_part'] = np.nan\n",
    "df_res"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "偏效应分析结果表明：\n",
    "- 在排除施肥方式的影响后，品种因子解释了产量误差的80.69%\n",
    "- 在排除品种的影响后，施肥方式因子解释了产量误差的78.18%\n",
    "\n",
    "主效应分析结果表明：\n",
    "- 品种因子单独解释了产量误差的47.70%\n",
    "- 施肥方式因子单独解释了产量误差的40.89%\n",
    "\n",
    "联合效应分析结果表明：\n",
    "- 将品种主效应量和施肥方式主效应量相加，得到的联合效应量为0.4770+0.4089=0.8859，表明品种和施肥方式两个因子联合起来总共解释产量误差的88.59%。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.3.3 交互效应分析"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "检验交互效应的假设：\n",
    "- $H_0: \\gamma_{ij} = 0, (i=1,2,\\cdots,I; j=1,2,\\cdots,J)$ (交互效应不显著)\n",
    "- $H_1: \\gamma_{ij}$ 至少有一个不等于0 (交互效应显著)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>品种</th>\n",
       "      <td>2.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>280.00</td>\n",
       "      <td>54.368932</td>\n",
       "      <td>1.220666e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>施肥方式</th>\n",
       "      <td>1.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>480.00</td>\n",
       "      <td>93.203883</td>\n",
       "      <td>9.729467e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>品种:施肥方式</th>\n",
       "      <td>2.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>5.20</td>\n",
       "      <td>1.009709</td>\n",
       "      <td>3.792836e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>24.0</td>\n",
       "      <td>123.6</td>\n",
       "      <td>5.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            df  sum_sq  mean_sq          F        PR(>F)\n",
       "品种         2.0   560.0   280.00  54.368932  1.220666e-09\n",
       "施肥方式       1.0   480.0   480.00  93.203883  9.729467e-10\n",
       "品种:施肥方式    2.0    10.4     5.20   1.009709  3.792836e-01\n",
       "Residual  24.0   123.6     5.15        NaN           NaN"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2i = ols('产量 ~ 品种 + 施肥方式 + 品种:施肥方式', data=example8_4).fit() # 拟合方差分析模型\n",
    "anova_lm(model2i, typ=1) # 执行方差分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>产量</td>        <th>  R-squared:         </th> <td>   0.895</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.873</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   40.79</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 14 May 2023</td> <th>  Prob (F-statistic):</th> <td>5.77e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:19:59</td>     <th>  Log-Likelihood:    </th> <td> -63.806</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    30</td>      <th>  AIC:               </th> <td>   139.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    24</td>      <th>  BIC:               </th> <td>   148.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>           <td>   87.8000</td> <td>    1.015</td> <td>   86.512</td> <td> 0.000</td> <td>   85.705</td> <td>   89.895</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>品种[T.品种2]</th>           <td>  -10.4000</td> <td>    1.435</td> <td>   -7.246</td> <td> 0.000</td> <td>  -13.362</td> <td>   -7.438</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>品种[T.品种3]</th>           <td>   -1.0000</td> <td>    1.435</td> <td>   -0.697</td> <td> 0.493</td> <td>   -3.962</td> <td>    1.962</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>施肥方式[T.甲]</th>           <td>   -7.6000</td> <td>    1.435</td> <td>   -5.295</td> <td> 0.000</td> <td>  -10.562</td> <td>   -4.638</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>品种[T.品种2]:施肥方式[T.甲]</th> <td>    0.8000</td> <td>    2.030</td> <td>    0.394</td> <td> 0.697</td> <td>   -3.389</td> <td>    4.989</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>品种[T.品种3]:施肥方式[T.甲]</th> <td>   -2.0000</td> <td>    2.030</td> <td>   -0.985</td> <td> 0.334</td> <td>   -6.189</td> <td>    2.189</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.127</td> <th>  Durbin-Watson:     </th> <td>   2.334</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.569</td> <th>  Jarque-Bera (JB):  </th> <td>   0.644</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.359</td> <th>  Prob(JB):          </th> <td>   0.725</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.007</td> <th>  Cond. No.          </th> <td>    9.77</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                     产量   R-squared:                       0.895\n",
       "Model:                            OLS   Adj. R-squared:                  0.873\n",
       "Method:                 Least Squares   F-statistic:                     40.79\n",
       "Date:                Sun, 14 May 2023   Prob (F-statistic):           5.77e-11\n",
       "Time:                        20:19:59   Log-Likelihood:                -63.806\n",
       "No. Observations:                  30   AIC:                             139.6\n",
       "Df Residuals:                      24   BIC:                             148.0\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=======================================================================================\n",
       "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------\n",
       "Intercept              87.8000      1.015     86.512      0.000      85.705      89.895\n",
       "品种[T.品种2]             -10.4000      1.435     -7.246      0.000     -13.362      -7.438\n",
       "品种[T.品种3]              -1.0000      1.435     -0.697      0.493      -3.962       1.962\n",
       "施肥方式[T.甲]              -7.6000      1.435     -5.295      0.000     -10.562      -4.638\n",
       "品种[T.品种2]:施肥方式[T.甲]     0.8000      2.030      0.394      0.697      -3.389       4.989\n",
       "品种[T.品种3]:施肥方式[T.甲]    -2.0000      2.030     -0.985      0.334      -6.189       2.189\n",
       "==============================================================================\n",
       "Omnibus:                        1.127   Durbin-Watson:                   2.334\n",
       "Prob(Omnibus):                  0.569   Jarque-Bera (JB):                0.644\n",
       "Skew:                          -0.359   Prob(JB):                        0.725\n",
       "Kurtosis:                       3.007   Cond. No.                         9.77\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2i.summary() # 显示方差分析结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "检验交互效应的P=0.379，大于0.05，表示交互效应对产量的影响不显著。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_col = '产量' # 因变量列名\n",
    "x1_col = '品种' # 自变量列名\n",
    "x2_col = '施肥方式' # 自变量列名\n",
    "\n",
    "plt.subplots(2, 2, figsize=(12, 10)) # 设置图形大小\n",
    "\n",
    "ax1 = plt.subplot(221) # 创建子图1\n",
    "interaction_plot(\n",
    "    x=example8_4[x2_col], trace=example8_4[x1_col], response=example8_4[y_col],\n",
    "    xlabel=x2_col, ylabel=y_col, ax=ax1\n",
    ") # 绘制交互作用图\n",
    "ax1.set_title(f'{y_col}~{x2_col}|{x1_col}') # 添加子图标题\n",
    "\n",
    "ax2 = plt.subplot(222) # 创建子图2\n",
    "example8_4.boxplot(column=y_col, by=x1_col, grid=False, ax=ax2) # 绘制箱线图\n",
    "ax2.set_title(f'{y_col}~{x1_col}|{x1_col}') # 添加子图标题\n",
    "\n",
    "ax3 = plt.subplot(223) # 创建子图3\n",
    "example8_4.boxplot(column=y_col, by=x2_col, grid=False, ax=ax3) # 绘制箱线图\n",
    "ax3.set_title(f'{y_col}~{x2_col}|{x2_col}') # 添加子图标题\n",
    "\n",
    "ax4 = plt.subplot(224) # 创建子图4\n",
    "interaction_plot(\n",
    "    x=example8_4[x1_col], trace=example8_4[x2_col], response=example8_4[y_col],\n",
    "    xlabel=x1_col, ylabel=y_col, ax=ax4\n",
    ") # 绘制交互作用图\n",
    "ax4.set_title(f'{y_col}~{x1_col}|{x2_col}') # 添加子图标题\n",
    "\n",
    "plt.show() # 显示图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "      <th>eta_sq</th>\n",
       "      <th>eta_sq_part</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>品种</th>\n",
       "      <td>2.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>280.00</td>\n",
       "      <td>54.368932</td>\n",
       "      <td>1.220666e-09</td>\n",
       "      <td>0.477002</td>\n",
       "      <td>0.819193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>施肥方式</th>\n",
       "      <td>1.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>480.00</td>\n",
       "      <td>93.203883</td>\n",
       "      <td>9.729467e-10</td>\n",
       "      <td>0.408859</td>\n",
       "      <td>0.795229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>品种:施肥方式</th>\n",
       "      <td>2.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>5.20</td>\n",
       "      <td>1.009709</td>\n",
       "      <td>3.792836e-01</td>\n",
       "      <td>0.008859</td>\n",
       "      <td>0.077612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>24.0</td>\n",
       "      <td>123.6</td>\n",
       "      <td>5.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.105281</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            df  sum_sq  mean_sq          F        PR(>F)    eta_sq   \n",
       "品种         2.0   560.0   280.00  54.368932  1.220666e-09  0.477002  \\\n",
       "施肥方式       1.0   480.0   480.00  93.203883  9.729467e-10  0.408859   \n",
       "品种:施肥方式    2.0    10.4     5.20   1.009709  3.792836e-01  0.008859   \n",
       "Residual  24.0   123.6     5.15        NaN           NaN  0.105281   \n",
       "\n",
       "          eta_sq_part  \n",
       "品种           0.819193  \n",
       "施肥方式         0.795229  \n",
       "品种:施肥方式      0.077612  \n",
       "Residual          NaN  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 交互效应方差分析的效应量\n",
    "df_res = anova_lm(model2i, typ=1) # 执行方差分析\n",
    "df_res['eta_sq'] = df_res['sum_sq'] / df_res['sum_sq'].sum() # 计算主效应量\n",
    "df_res['eta_sq_part'] = df_res['sum_sq'] / (df_res['sum_sq'] + df_res.loc['Residual', 'sum_sq']) # 计算偏效应量\n",
    "df_res.loc['Residual', 'eta_sq_part'] = np.nan\n",
    "df_res"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "偏效应分析结果表明：\n",
    "- 在排除施肥方式和交互效应的影响后，品种因子解释了产量误差的81.92%\n",
    "- 在排除品种和交互效应的影响后，施肥方式因子解释了产量误差的79.52%\n",
    "- 在排除品种和施肥方式的影响后，交互效应解释了产量误差的7.76%"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.4 方差分析的假定及其检验"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.4.1 正态性检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>品种1</th>\n",
       "      <th>品种2</th>\n",
       "      <th>品种3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81</td>\n",
       "      <td>71</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82</td>\n",
       "      <td>72</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>79</td>\n",
       "      <td>72</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>81</td>\n",
       "      <td>66</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78</td>\n",
       "      <td>72</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>89</td>\n",
       "      <td>77</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>92</td>\n",
       "      <td>81</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>87</td>\n",
       "      <td>77</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>85</td>\n",
       "      <td>73</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>86</td>\n",
       "      <td>79</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   品种1  品种2  品种3\n",
       "0   81   71   76\n",
       "1   82   72   79\n",
       "2   79   72   77\n",
       "3   81   66   76\n",
       "4   78   72   78\n",
       "5   89   77   89\n",
       "6   92   81   87\n",
       "7   87   77   84\n",
       "8   85   73   87\n",
       "9   86   79   87"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example8_1 = pd.read_csv('./pydata/chap08/example8_1.csv', encoding='gbk') # 读取数据\n",
    "example8_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 每个品种产量的正态Q-Q图\n",
    "plt.subplots(1, 3, figsize=(16, 5)) # 设置图形大小\n",
    "\n",
    "ax1 = plt.subplot(131) # 创建子图1\n",
    "pplot_1 = sm.ProbPlot(example8_1['品种1'], fit=True) # 创建概率图对象\n",
    "pplot_1.qqplot(line='r', ax=ax1, xlabel='期望正态值', ylabel='标准化的观测值') # 绘制概率图\n",
    "ax1.set_title('品种1的Q-Q图', fontsize=15) # 添加子图标题\n",
    "\n",
    "ax2 = plt.subplot(132) # 创建子图2\n",
    "ppplot_2 = sm.ProbPlot(example8_1['品种2'], fit=True) # 创建概率图对象\n",
    "ppplot_2.qqplot(line='r', ax=ax2, xlabel='期望正态值', ylabel='标准化的观测值') # 绘制概率图\n",
    "ax2.set_title('品种2的Q-Q图', fontsize=15) # 添加子图标题\n",
    "\n",
    "ax3 = plt.subplot(133) # 创建子图3\n",
    "ppplot_3 = sm.ProbPlot(example8_1['品种3'], fit=True) # 创建概率图对象\n",
    "ppplot_3.qqplot(line='r', ax=ax3, xlabel='期望正态值', ylabel='标准化的观测值') # 绘制概率图\n",
    "ax3.set_title('品种3的Q-Q图', fontsize=15) # 添加子图标题\n",
    "\n",
    "plt.show() # 显示图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>品种</th>\n",
       "      <th>产量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>品种1</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>品种1</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>品种1</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>品种1</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>品种1</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    品种  产量\n",
       "0  品种1  81\n",
       "1  品种1  82\n",
       "2  品种1  79\n",
       "3  品种1  81\n",
       "4  品种1  78"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example8_2 = pd.read_csv('./pydata/chap08/example8_2.csv', encoding='gbk') # 读取数据\n",
    "example8_2.head() # 显示前5行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3个品种产量数据合并后的正态Q-Q图、直方图及核密度曲线\n",
    "plt.subplots(1, 2, figsize=(12, 5)) # 设置图形大小\n",
    "\n",
    "ax1 = plt.subplot(121) # 创建子图1\n",
    "pplot = sm.ProbPlot(example8_2['产量'], fit=True) # 创建概率图对象\n",
    "pplot.qqplot(line='r', ax=ax1, xlabel='期望正态值', ylabel='标准化的观测值') # 绘制概率图\n",
    "\n",
    "ax2 = plt.subplot(122) # 创建子图2\n",
    "example8_2['产量'].plot(kind='hist', bins=5, ax=ax2, density=True, legend=False) # 绘制直方图\n",
    "example8_2['产量'].plot(kind='density', ax=ax2, linewidth=3) # 绘制核密度曲线\n",
    "ax2.set_xlim(65, 93)\n",
    "\n",
    "plt.show() # 显示图形"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提出假设：\n",
    "- $H_0:$ 产量服从正态分布\n",
    "- $H_1:$ 产量不服从正态分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "统计量W=0.97299, p值=0.6237\n"
     ]
    }
   ],
   "source": [
    "W, p_value = shapiro(example8_2['产量']) # 执行S-W检验\n",
    "print(f'统计量W={W:.5f}, p值={p_value:.4g}') # 显示检验结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "统计量D=0.09771, p值=0.9369\n"
     ]
    }
   ],
   "source": [
    "D, p_value = kstest(\n",
    "    example8_2['产量'], 'norm', alternative='two-sided', mode='asymp',\n",
    "    args=(example8_2['产量'].mean(), example8_2['产量'].std())\n",
    ") # 执行K-S检验\n",
    "print(f'统计量D={D:.5f}, p值={p_value:.4g}') # 显示检验结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P>0.05，所以不能拒绝原假设。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.4.2 方差齐性检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = ols('产量 ~ 品种', data=example8_2).fit() # 拟合方差分析模型\n",
    "plt.subplots(1, 2, figsize=(12, 5)) # 设置图形大小\n",
    "\n",
    "plt.subplot(121) # 创建子图1\n",
    "plt.scatter(model.fittedvalues, model.resid) # 绘制残差图\n",
    "plt.xlabel('拟合值') # 添加横轴标签\n",
    "plt.ylabel('残差') # 添加纵轴标签\n",
    "plt.title('残差值与拟合值图', fontsize=15) # 添加子图标题\n",
    "plt.axhline(0, ls='--') # 添加水平参考线\n",
    "\n",
    "ax2 = plt.subplot(122) # 创建子图2\n",
    "pplot = sm.ProbPlot(model.resid, fit=True) # 创建概率图对象\n",
    "pplot.qqplot(line='r', ax=ax2, xlabel='期望正态值', ylabel='标准化的残差') # 绘制概率图\n",
    "ax2.set_title('残差的Q-Q图', fontsize=15) # 添加子图标题\n",
    "\n",
    "plt.show() # 显示图形"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提出假设：\n",
    "- $H_0: \\sigma^2_1 = \\sigma^2_2 = \\cdots = \\sigma^2_I$ (产量的方差齐性成立)\n",
    "- $H_1: \\sigma^2_1, \\sigma^2_2, \\cdots, \\sigma^2_I$ 不全相等 (产量的方差齐性不成立)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>施肥方式</th>\n",
       "      <th>品种</th>\n",
       "      <th>产量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>甲</td>\n",
       "      <td>品种1</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>甲</td>\n",
       "      <td>品种1</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>甲</td>\n",
       "      <td>品种1</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>甲</td>\n",
       "      <td>品种1</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>甲</td>\n",
       "      <td>品种1</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  施肥方式   品种  产量\n",
       "0    甲  品种1  81\n",
       "1    甲  品种1  82\n",
       "2    甲  品种1  79\n",
       "3    甲  品种1  81\n",
       "4    甲  品种1  78"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example8_4 = pd.read_csv('./pydata/chap08/example8_4.csv', encoding='gbk') # 读取数据\n",
    "example8_4.head() # 显示前5行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "统计量B=0.30152, p值=0.8601\n"
     ]
    }
   ],
   "source": [
    "sample1 = example8_4.loc[example8_4['品种']=='品种1', '产量'].values # 品种1的产量数据\n",
    "sample2 = example8_4.loc[example8_4['品种']=='品种2', '产量'].values # 品种2的产量数据\n",
    "sample3 = example8_4.loc[example8_4['品种']=='品种3', '产量'].values # 品种3的产量数据\n",
    "\n",
    "B, p_value = bartlett(sample1, sample2, sample3) # 执行Bartlett检验\n",
    "print(f'统计量B={B:.5f}, p值={p_value:.4g}') # 显示检验结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "统计量B=0.42431, p值=0.5148\n"
     ]
    }
   ],
   "source": [
    "sample1 = example8_4.loc[example8_4['施肥方式']=='甲', '产量'].values # 施肥方式甲的产量数据\n",
    "sample2 = example8_4.loc[example8_4['施肥方式']=='乙', '产量'].values # 施肥方式乙的产量数据\n",
    "\n",
    "B, p_value = bartlett(sample1, sample2) # 执行Bartlett检验\n",
    "print(f'统计量B={B:.5f}, p值={p_value:.4g}') # 显示检验结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P>0.05，所以不能拒绝原假设。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "统计量F=0.92919, p值=0.4071\n"
     ]
    }
   ],
   "source": [
    "sample1 = example8_4.loc[example8_4['品种']=='品种1', '产量'].values # 品种1的产量数据\n",
    "sample2 = example8_4.loc[example8_4['品种']=='品种2', '产量'].values # 品种2的产量数据\n",
    "sample3 = example8_4.loc[example8_4['品种']=='品种3', '产量'].values # 品种3的产量数据\n",
    "\n",
    "F, p_value = levene(sample1, sample2, sample3) # 执行Levene检验\n",
    "print(f'统计量F={F:.5f}, p值={p_value:.4g}') # 显示检验结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "统计量F=0.23228, p值=0.6336\n"
     ]
    }
   ],
   "source": [
    "sample1 = example8_4.loc[example8_4['施肥方式']=='甲', '产量'].values # 施肥方式甲的产量数据\n",
    "sample2 = example8_4.loc[example8_4['施肥方式']=='乙', '产量'].values # 施肥方式乙的产量数据\n",
    "\n",
    "F, p_value = levene(sample1, sample2) # 执行Levene检验\n",
    "print(f'统计量F={F:.5f}, p值={p_value:.4g}') # 显示检验结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P>0.05，所以不能拒绝原假设。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 习题"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>机器1</th>\n",
       "      <th>机器2</th>\n",
       "      <th>机器3</th>\n",
       "      <th>机器4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.05</td>\n",
       "      <td>3.99</td>\n",
       "      <td>3.97</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.01</td>\n",
       "      <td>4.02</td>\n",
       "      <td>3.98</td>\n",
       "      <td>4.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.02</td>\n",
       "      <td>4.01</td>\n",
       "      <td>3.97</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.04</td>\n",
       "      <td>3.99</td>\n",
       "      <td>3.95</td>\n",
       "      <td>4.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.02</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.03</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    机器1   机器2   机器3   机器4\n",
       "0  4.05  3.99  3.97  4.00\n",
       "1  4.01  4.02  3.98  4.02\n",
       "2  4.02  4.01  3.97  3.99\n",
       "3  4.04  3.99  3.95  4.01\n",
       "4  4.00  4.00  4.02  4.00\n",
       "5  4.03  4.05  4.00  4.03"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./exercise/chap08/exercise8_1.csv', encoding='gbk') # 读取数据\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>机器</th>\n",
       "      <th>装填量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>机器1</td>\n",
       "      <td>4.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>机器1</td>\n",
       "      <td>4.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>机器1</td>\n",
       "      <td>4.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>机器1</td>\n",
       "      <td>4.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>机器1</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    机器   装填量\n",
       "0  机器1  4.05\n",
       "1  机器1  4.01\n",
       "2  机器1  4.02\n",
       "3  机器1  4.04\n",
       "4  机器1  4.00"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (1) 检验机器对装填量是否有显著影响（α=0.01）。\n",
    "df_melt = pd.melt(\n",
    "    df, value_vars=['机器1', '机器2', '机器3', '机器4'], \n",
    "    value_name='装填量', var_name='机器'\n",
    ") # 数据转换\n",
    "df_melt.head() # 显示前5行数据"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提出假设：\n",
    "- $H_0: \\alpha_1 = \\alpha_2 = \\alpha_3 = \\alpha_4 = 0$(机器对装填量的影响不显著)\n",
    "- $H_1: \\alpha_1, \\alpha_2, \\alpha_3, \\alpha_4$ 至少有一个不等于0(机器对装填量的影响显著)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>机器</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.005846</td>\n",
       "      <td>0.001949</td>\n",
       "      <td>4.575995</td>\n",
       "      <td>0.013485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.008517</td>\n",
       "      <td>0.000426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            df    sum_sq   mean_sq         F    PR(>F)\n",
       "机器         3.0  0.005846  0.001949  4.575995  0.013485\n",
       "Residual  20.0  0.008517  0.000426       NaN       NaN"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ols('装填量 ~ 机器', data=df_melt).fit() # 拟合方差分析模型\n",
    "anova_table = anova_lm(model, typ=1) # 方差分析表\n",
    "anova_table # 显示方差分析表"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P>0.01, 所以不能拒绝原假设。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>装填量</td>       <th>  R-squared:         </th> <td>   0.407</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.318</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   4.576</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 16 May 2023</td> <th>  Prob (F-statistic):</th>  <td>0.0135</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>10:28:24</td>     <th>  Log-Likelihood:    </th> <td>  61.271</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    24</td>      <th>  AIC:               </th> <td>  -114.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    20</td>      <th>  BIC:               </th> <td>  -109.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    4.0250</td> <td>    0.008</td> <td>  477.773</td> <td> 0.000</td> <td>    4.007</td> <td>    4.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>机器[T.机器2]</th> <td>   -0.0150</td> <td>    0.012</td> <td>   -1.259</td> <td> 0.223</td> <td>   -0.040</td> <td>    0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>机器[T.机器3]</th> <td>   -0.0433</td> <td>    0.012</td> <td>   -3.637</td> <td> 0.002</td> <td>   -0.068</td> <td>   -0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>机器[T.机器4]</th> <td>   -0.0167</td> <td>    0.012</td> <td>   -1.399</td> <td> 0.177</td> <td>   -0.042</td> <td>    0.008</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.421</td> <th>  Durbin-Watson:     </th> <td>   2.490</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.491</td> <th>  Jarque-Bera (JB):  </th> <td>   1.277</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.499</td> <th>  Prob(JB):          </th> <td>   0.528</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.468</td> <th>  Cond. No.          </th> <td>    4.79</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    装填量   R-squared:                       0.407\n",
       "Model:                            OLS   Adj. R-squared:                  0.318\n",
       "Method:                 Least Squares   F-statistic:                     4.576\n",
       "Date:                Tue, 16 May 2023   Prob (F-statistic):             0.0135\n",
       "Time:                        10:28:24   Log-Likelihood:                 61.271\n",
       "No. Observations:                  24   AIC:                            -114.5\n",
       "Df Residuals:                      20   BIC:                            -109.8\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      4.0250      0.008    477.773      0.000       4.007       4.043\n",
       "机器[T.机器2]     -0.0150      0.012     -1.259      0.223      -0.040       0.010\n",
       "机器[T.机器3]     -0.0433      0.012     -3.637      0.002      -0.068      -0.018\n",
       "机器[T.机器4]     -0.0167      0.012     -1.399      0.177      -0.042       0.008\n",
       "==============================================================================\n",
       "Omnibus:                        1.421   Durbin-Watson:                   2.490\n",
       "Prob(Omnibus):                  0.491   Jarque-Bera (JB):                1.277\n",
       "Skew:                           0.499   Prob(JB):                        0.528\n",
       "Kurtosis:                       2.468   Cond. No.                         4.79\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary() # 显示模型汇总信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "效应量为 0.407\n"
     ]
    }
   ],
   "source": [
    "# (2) 分析效应量\n",
    "print(f'效应量为 {model.rsquared:.4g}') # 显示效应量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Multiple Comparison of Means - Tukey HSD, FWER=0.01</caption>\n",
       "<tr>\n",
       "  <th>group1</th> <th>group2</th> <th>meandiff</th>  <th>p-adj</th>  <th>lower</th>   <th>upper</th>  <th>reject</th>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>机器1</td>    <td>机器2</td>   <td>-0.015</td>  <td>0.5982</td> <td>-0.0573</td> <td>0.0273</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>机器1</td>    <td>机器3</td>   <td>-0.0433</td> <td>0.0082</td> <td>-0.0856</td> <td>-0.0011</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>机器1</td>    <td>机器4</td>   <td>-0.0167</td> <td>0.5144</td> <td>-0.0589</td> <td>0.0256</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>机器2</td>    <td>机器3</td>   <td>-0.0283</td>  <td>0.114</td> <td>-0.0706</td> <td>0.0139</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>机器2</td>    <td>机器4</td>   <td>-0.0017</td>  <td>0.999</td> <td>-0.0439</td> <td>0.0406</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>机器3</td>    <td>机器4</td>   <td>0.0267</td>  <td>0.1471</td> <td>-0.0156</td> <td>0.0689</td>   <td>False</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (3) 采用HSD方法比较哪些机器的装填量之间存在显著差异。\n",
    "mc = MultiComparison(df_melt['装填量'], df_melt['机器']) # 创建多重比较对象\n",
    "mc.tukeyhsd(alpha=0.01).summary() # 执行多重比较"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结果显示，机器1与机器3之间的差异显著，其余两两之间的差异均不显著。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提出正态性假设：\n",
    "- $H_0:$ 装填量服从正态分布\n",
    "- $H_1:$ 装填量不服从正态分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "统计量W=0.97509, p值=0.7912\n",
      "统计量D=0.10958, p值=0.9354\n"
     ]
    }
   ],
   "source": [
    "# (4) 检验装填量是否满足正态性和方差齐性。\n",
    "# S-W检验\n",
    "W, p_value = shapiro(df_melt['装填量']) # 执行S-W检验\n",
    "print(f'统计量W={W:.5f}, p值={p_value:.4g}') # 显示检验结果\n",
    "\n",
    "# K-S检验\n",
    "D, p_value = kstest(\n",
    "    df_melt['装填量'], 'norm', alternative='two-sided', mode='asymp',\n",
    "    args=(df_melt['装填量'].mean(), df_melt['装填量'].std())\n",
    ") # 执行K-S检验\n",
    "print(f'统计量D={D:.5f}, p值={p_value:.4g}') # 显示检验结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P>0.05，所以不能拒绝原假设。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提出方差齐性假设：\n",
    "- $H_0: \\sigma^2_1 = \\sigma^2_2 = \\sigma^2_3 = \\sigma^2_4$ (装填量的方差齐性成立)\n",
    "- $H_1: \\sigma^2_1, \\sigma^2_2, \\sigma^2_3, \\sigma^2_4$ 不全相等 (装填量的方差齐性不成立)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "统计量B=1.39265, p值=0.7073\n",
      "统计量F=0.30864, p值=0.8189\n"
     ]
    }
   ],
   "source": [
    "sample1 = df_melt.loc[df_melt['机器']=='机器1', '装填量'].values # 机器1的装填量数据\n",
    "sample2 = df_melt.loc[df_melt['机器']=='机器2', '装填量'].values # 机器2的装填量数据\n",
    "sample3 = df_melt.loc[df_melt['机器']=='机器3', '装填量'].values # 机器3的装填量数据\n",
    "sample4 = df_melt.loc[df_melt['机器']=='机器4', '装填量'].values # 机器4的装填量数据\n",
    "\n",
    "# Bartlett检验\n",
    "B, p_value = bartlett(sample1, sample2, sample3, sample4) # 执行Bartlett检验\n",
    "print(f'统计量B={B:.5f}, p值={p_value:.4g}') # 显示检验结果\n",
    "\n",
    "# Levene检验\n",
    "F, p_value = levene(sample1, sample2, sample3, sample4) # 执行Levene检验\n",
    "print(f'统计量F={F:.5f}, p值={p_value:.4g}') # 显示检验结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P>0.05，所以不能拒绝原假设。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>高层管理者</th>\n",
       "      <th>中层管理者</th>\n",
       "      <th>低层管理者</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   高层管理者  中层管理者  低层管理者\n",
       "0      7      8      5\n",
       "1      7      9      6\n",
       "2      8      8      5\n",
       "3      7     10      7\n",
       "4      9      9      4\n",
       "5      8     10      8"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./exercise/chap08/exercise8_2.csv', encoding='gbk') # 读取数据\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>管理者</th>\n",
       "      <th>满意度得分</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>高层管理者</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>高层管理者</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>高层管理者</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>高层管理者</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>高层管理者</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     管理者  满意度得分\n",
       "0  高层管理者      7\n",
       "1  高层管理者      7\n",
       "2  高层管理者      8\n",
       "3  高层管理者      7\n",
       "4  高层管理者      9"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (1) 检验管理者的水平不同是否会导致评分的显著差异（α=0.05），并分析效应量。\n",
    "df_melt = pd.melt(\n",
    "    df, value_vars=['高层管理者', '中层管理者', '低层管理者'],\n",
    "    value_name='满意度得分', var_name='管理者'\n",
    ") # 数据转换\n",
    "df_melt.head() # 显示前5行数据"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提出假设：\n",
    "- $H_0: \\alpha_1 = \\alpha_2 = \\alpha_3 = 0$(管理者类型对满意度评分的影响不显著)\n",
    "- $H_1: \\alpha_1, \\alpha_2, \\alpha_3$ 至少有一个不等于0(管理者类型对满意度评分的影响显著)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>管理者</th>\n",
       "      <td>2.0</td>\n",
       "      <td>30.333333</td>\n",
       "      <td>15.166667</td>\n",
       "      <td>12.522936</td>\n",
       "      <td>0.000633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>15.0</td>\n",
       "      <td>18.166667</td>\n",
       "      <td>1.211111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            df     sum_sq    mean_sq          F    PR(>F)\n",
       "管理者        2.0  30.333333  15.166667  12.522936  0.000633\n",
       "Residual  15.0  18.166667   1.211111        NaN       NaN"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ols('满意度得分 ~ 管理者', data=df_melt).fit() # 拟合方差分析模型\n",
    "anova_table = anova_lm(model, typ=1) # 方差分析表\n",
    "anova_table # 显示方差分析表"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P<0.05, 所以拒绝原假设。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zmy/mambaforge/lib/python3.10/site-packages/scipy/stats/_stats_py.py:1736: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=18\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>满意度得分</td>      <th>  R-squared:         </th> <td>   0.625</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.575</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   12.52</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 16 May 2023</td> <th>  Prob (F-statistic):</th> <td>0.000633</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>10:45:58</td>     <th>  Log-Likelihood:    </th> <td> -25.624</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    18</td>      <th>  AIC:               </th> <td>   57.25</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    15</td>      <th>  BIC:               </th> <td>   59.92</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>    <td>    9.0000</td> <td>    0.449</td> <td>   20.032</td> <td> 0.000</td> <td>    8.042</td> <td>    9.958</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>管理者[T.低层管理者]</th> <td>   -3.1667</td> <td>    0.635</td> <td>   -4.984</td> <td> 0.000</td> <td>   -4.521</td> <td>   -1.812</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>管理者[T.高层管理者]</th> <td>   -1.3333</td> <td>    0.635</td> <td>   -2.098</td> <td> 0.053</td> <td>   -2.688</td> <td>    0.021</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.544</td> <th>  Durbin-Watson:     </th> <td>   2.815</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.762</td> <th>  Jarque-Bera (JB):  </th> <td>   0.598</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.329</td> <th>  Prob(JB):          </th> <td>   0.742</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.396</td> <th>  Cond. No.          </th> <td>    3.73</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  满意度得分   R-squared:                       0.625\n",
       "Model:                            OLS   Adj. R-squared:                  0.575\n",
       "Method:                 Least Squares   F-statistic:                     12.52\n",
       "Date:                Tue, 16 May 2023   Prob (F-statistic):           0.000633\n",
       "Time:                        10:45:58   Log-Likelihood:                -25.624\n",
       "No. Observations:                  18   AIC:                             57.25\n",
       "Df Residuals:                      15   BIC:                             59.92\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "================================================================================\n",
       "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "Intercept        9.0000      0.449     20.032      0.000       8.042       9.958\n",
       "管理者[T.低层管理者]    -3.1667      0.635     -4.984      0.000      -4.521      -1.812\n",
       "管理者[T.高层管理者]    -1.3333      0.635     -2.098      0.053      -2.688       0.021\n",
       "==============================================================================\n",
       "Omnibus:                        0.544   Durbin-Watson:                   2.815\n",
       "Prob(Omnibus):                  0.762   Jarque-Bera (JB):                0.598\n",
       "Skew:                           0.329   Prob(JB):                        0.742\n",
       "Kurtosis:                       2.396   Cond. No.                         3.73\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary() # 显示模型汇总信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "效应量为 0.6254\n"
     ]
    }
   ],
   "source": [
    "print(f'效应量为 {model.rsquared:.4g}') # 显示效应量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Multiple Comparison of Means - Tukey HSD, FWER=0.05</caption>\n",
       "<tr>\n",
       "  <th>group1</th> <th>group2</th> <th>meandiff</th>  <th>p-adj</th>  <th>lower</th>   <th>upper</th>  <th>reject</th>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>中层管理者</td>  <td>低层管理者</td>  <td>-3.1667</td> <td>0.0005</td> <td>-4.817</td>  <td>-1.5163</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>中层管理者</td>  <td>高层管理者</td>  <td>-1.3333</td> <td>0.1238</td> <td>-2.9837</td>  <td>0.317</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>低层管理者</td>  <td>高层管理者</td>  <td>1.8333</td>  <td>0.0288</td>  <td>0.183</td>  <td>3.4837</td>   <td>True</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (2) 采用HSD方法比较管理者的评分之间的差异。\n",
    "mc = MultiComparison(df_melt['满意度得分'], df_melt['管理者']) # 创建多重比较对象\n",
    "mc.tukeyhsd(alpha=0.05).summary() # 执行多重比较"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结果显示，中层管理者与低层管理者、低层管理者与高层管理者之间的差异显著，中层管理者与高层管理者之间的差异不显著。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提出正态性假设：\n",
    "- $H_0:$ 满意度评分服从正态分布\n",
    "- $H_1:$ 满意度评分不服从正态分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "统计量W=0.94141, p值=0.3063\n",
      "统计量D=0.17195, p值=0.6617\n"
     ]
    }
   ],
   "source": [
    "# (3) 检验满意度评分是否满足正态性和方差齐性。\n",
    "# S-W检验\n",
    "W, p_value = shapiro(df_melt['满意度得分']) # 执行S-W检验\n",
    "print(f'统计量W={W:.5f}, p值={p_value:.4g}') # 显示检验结果\n",
    "\n",
    "# K-S检验\n",
    "D, p_value = kstest(\n",
    "    df_melt['满意度得分'], 'norm', alternative='two-sided', mode='asymp',\n",
    "    args=(df_melt['满意度得分'].mean(), df_melt['满意度得分'].std())\n",
    ") # 执行K-S检验\n",
    "print(f'统计量D={D:.5f}, p值={p_value:.4g}') # 显示检验结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P>0.05，所以不能拒绝原假设。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提出方差齐性假设：\n",
    "- $H_0: \\sigma^2_1 = \\sigma^2_2 = \\sigma^2_3$ (满意度评分的方差齐性成立)\n",
    "- $H_1: \\sigma^2_1, \\sigma^2_2, \\sigma^2_3$ 不全相等 (满意度评分的方差齐性不成立)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "统计量B=1.97464, p值=0.3726\n",
      "统计量F=1.36364, p值=0.2857\n"
     ]
    }
   ],
   "source": [
    "sample1 = df_melt.loc[df_melt['管理者']=='高层管理者', '满意度得分'].values # 高层管理者满意度得分数据\n",
    "sample2 = df_melt.loc[df_melt['管理者']=='中层管理者', '满意度得分'].values # 中层管理者满意度得分数据\n",
    "sample3 = df_melt.loc[df_melt['管理者']=='低层管理者', '满意度得分'].values # 低层管理者满意度得分数据\n",
    "\n",
    "# Bartlett检验\n",
    "B, p_value = bartlett(sample1, sample2, sample3) # 执行Bartlett检验\n",
    "print(f'统计量B={B:.5f}, p值={p_value:.4g}') # 显示检验结果\n",
    "\n",
    "# Levene检验\n",
    "F, p_value = levene(sample1, sample2, sample3) # 执行Levene检验\n",
    "print(f'统计量F={F:.5f}, p值={p_value:.4g}') # 显示检验结果"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P>0.05，所以不能拒绝原假设。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>实验号</th>\n",
       "      <th>企业A</th>\n",
       "      <th>企业B</th>\n",
       "      <th>企业C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>28</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>43</td>\n",
       "      <td>30</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "      <td>34</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>39</td>\n",
       "      <td>26</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   实验号  企业A  企业B  企业C\n",
       "0    1   50   32   45\n",
       "1    2   50   28   42\n",
       "2    3   43   30   38\n",
       "3    4   40   34   48\n",
       "4    5   39   26   40"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./exercise/chap08/exercise8_3.csv', encoding='gbk') # 读取数据\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>企业</th>\n",
       "      <th>电池使用寿命</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>企业A</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>企业A</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>企业A</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>企业A</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>企业A</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    企业  电池使用寿命\n",
       "0  企业A      50\n",
       "1  企业A      50\n",
       "2  企业A      43\n",
       "3  企业A      40\n",
       "4  企业A      39"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (1) 分析3个企业生产的电池平均使用寿命之间有无显著差异（α=0.05），并分析效应量。\n",
    "df_melt = pd.melt(\n",
    "    df, value_vars=['企业A', '企业B', '企业C'],\n",
    "    value_name='电池使用寿命', var_name='企业'\n",
    ") # 数据转换\n",
    "df_melt.head() # 显示前5行数据"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提出假设：\n",
    "- $H_0: \\alpha_1 = \\alpha_2 = \\alpha_3 = 0$(企业对电池使用寿命的影响不显著)\n",
    "- $H_1: \\alpha_1, \\alpha_2, \\alpha_3$ 至少有一个不等于0(企业对电池使用寿命的影响显著)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>企业</th>\n",
       "      <td>2.0</td>\n",
       "      <td>615.6</td>\n",
       "      <td>307.800000</td>\n",
       "      <td>17.068392</td>\n",
       "      <td>0.00031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>12.0</td>\n",
       "      <td>216.4</td>\n",
       "      <td>18.033333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            df  sum_sq     mean_sq          F   PR(>F)\n",
       "企业         2.0   615.6  307.800000  17.068392  0.00031\n",
       "Residual  12.0   216.4   18.033333        NaN      NaN"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ols('电池使用寿命 ~ 企业', data=df_melt).fit() # 拟合方差分析模型\n",
    "anova_table = anova_lm(model, typ=1) # 方差分析表\n",
    "anova_table # 显示方差分析表"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P<0.05, 所以拒绝原假设。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Multiple Comparison of Means - Tukey HSD, FWER=0.05</caption>\n",
       "<tr>\n",
       "  <th>group1</th> <th>group2</th> <th>meandiff</th>  <th>p-adj</th>   <th>lower</th>   <th>upper</th>  <th>reject</th>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>企业A</td>    <td>企业B</td>    <td>-14.4</td>  <td>0.0005</td> <td>-21.5653</td> <td>-7.2347</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>企业A</td>    <td>企业C</td>    <td>-1.8</td>   <td>0.7847</td>  <td>-8.9653</td> <td>5.3653</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>企业B</td>    <td>企业C</td>    <td>12.6</td>   <td>0.0014</td>  <td>5.4347</td>  <td>19.7653</td>  <td>True</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (2) 如果有差异，用HSD方法检验哪些企业之间有差异。\n",
    "mc = MultiComparison(df_melt['电池使用寿命'], df_melt['企业']) # 创建多重比较对象\n",
    "mc.tukeyhsd(alpha=0.05).summary() # 执行多重比较"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结果显示，企业A与企业B、企业B与企业C之间的差异显著，企业A与企业C之间的差异不显著。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>品种</th>\n",
       "      <th>施肥方案1</th>\n",
       "      <th>施肥方案2</th>\n",
       "      <th>施肥方案3</th>\n",
       "      <th>施肥方案4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>13.7</td>\n",
       "      <td>11.5</td>\n",
       "      <td>12.4</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>14.3</td>\n",
       "      <td>12.3</td>\n",
       "      <td>11.4</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>14.2</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.1</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   品种  施肥方案1  施肥方案2  施肥方案3  施肥方案4\n",
       "0   1   12.0    9.5   10.4    9.7\n",
       "1   2   13.7   11.5   12.4    9.6\n",
       "2   3   14.3   12.3   11.4   11.1\n",
       "3   4   14.2   14.0   12.5   12.0\n",
       "4   5   13.0   14.0   13.1   11.4"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./exercise/chap08/exercise8_4.csv', encoding='gbk') # 读取数据\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>品种</th>\n",
       "      <th>施肥方案</th>\n",
       "      <th>产量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>施肥方案1</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>施肥方案1</td>\n",
       "      <td>13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>施肥方案1</td>\n",
       "      <td>14.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>施肥方案1</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>施肥方案1</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   品种   施肥方案    产量\n",
       "0   1  施肥方案1  12.0\n",
       "1   2  施肥方案1  13.7\n",
       "2   3  施肥方案1  14.3\n",
       "3   4  施肥方案1  14.2\n",
       "4   5  施肥方案1  13.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (1) 检验不同品种和施肥方案对产量的影响是否显著（α=0.05）。\n",
    "df_melt = pd.melt(\n",
    "    df, id_vars=['品种'], value_vars=['施肥方案1', '施肥方案2', '施肥方案3', '施肥方案4'],\n",
    "    value_name='产量', var_name='施肥方案'\n",
    ") # 数据转换\n",
    "df_melt.head() # 显示前5行数据"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提出对于品种的假设：\n",
    "- $H_0: \\alpha_1 = \\cdots = \\alpha_i, i = 1, 2, 3, 4, 5$(品种对产量的影响不显著) \n",
    "- $H_1: \\alpha_1, \\cdots, \\alpha_i$ 不全相等(品种对产量的影响显著)\n",
    "\n",
    "提出对于施肥方案的假设：\n",
    "- $H_0: \\beta_1 = \\cdots = \\beta_j, j = 1, 2, 3, 4$(施肥方案对产量的影响不显著)\n",
    "- $H_1: \\beta_1, \\cdots, \\beta_j$ 不全相等(施肥方案对产量的影响显著)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(品种)</th>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0670</td>\n",
       "      <td>4.766750</td>\n",
       "      <td>7.239716</td>\n",
       "      <td>0.003315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>施肥方案</th>\n",
       "      <td>3.0</td>\n",
       "      <td>18.1815</td>\n",
       "      <td>6.060500</td>\n",
       "      <td>9.204658</td>\n",
       "      <td>0.001949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>12.0</td>\n",
       "      <td>7.9010</td>\n",
       "      <td>0.658417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            df   sum_sq   mean_sq         F    PR(>F)\n",
       "C(品种)      4.0  19.0670  4.766750  7.239716  0.003315\n",
       "施肥方案       3.0  18.1815  6.060500  9.204658  0.001949\n",
       "Residual  12.0   7.9010  0.658417       NaN       NaN"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ols('产量 ~ C(品种) + 施肥方案', data=df_melt).fit() # 拟合方差分析模型\n",
    "anova_table = anova_lm(model, typ=1) # 方差分析表\n",
    "anova_table # 显示方差分析表"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于P<0.05, 所以均拒绝原假设。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>产量</td>        <th>  R-squared:         </th> <td>   0.825</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.723</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   8.082</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 16 May 2023</td> <th>  Prob (F-statistic):</th> <td>0.000955</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:06:20</td>     <th>  Log-Likelihood:    </th> <td> -19.091</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    20</td>      <th>  AIC:               </th> <td>   54.18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    12</td>      <th>  BIC:               </th> <td>   62.15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     7</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>     <td>   11.7350</td> <td>    0.513</td> <td>   22.867</td> <td> 0.000</td> <td>   10.617</td> <td>   12.853</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(品种)[T.2]</th>    <td>    1.4000</td> <td>    0.574</td> <td>    2.440</td> <td> 0.031</td> <td>    0.150</td> <td>    2.650</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(品种)[T.3]</th>    <td>    1.8750</td> <td>    0.574</td> <td>    3.268</td> <td> 0.007</td> <td>    0.625</td> <td>    3.125</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(品种)[T.4]</th>    <td>    2.7750</td> <td>    0.574</td> <td>    4.836</td> <td> 0.000</td> <td>    1.525</td> <td>    4.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(品种)[T.5]</th>    <td>    2.4750</td> <td>    0.574</td> <td>    4.314</td> <td> 0.001</td> <td>    1.225</td> <td>    3.725</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>施肥方案[T.施肥方案2]</th> <td>   -1.1800</td> <td>    0.513</td> <td>   -2.299</td> <td> 0.040</td> <td>   -2.298</td> <td>   -0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>施肥方案[T.施肥方案3]</th> <td>   -1.4800</td> <td>    0.513</td> <td>   -2.884</td> <td> 0.014</td> <td>   -2.598</td> <td>   -0.362</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>施肥方案[T.施肥方案4]</th> <td>   -2.6800</td> <td>    0.513</td> <td>   -5.222</td> <td> 0.000</td> <td>   -3.798</td> <td>   -1.562</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.845</td> <th>  Durbin-Watson:     </th> <td>   1.350</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.398</td> <th>  Jarque-Bera (JB):  </th> <td>   1.241</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.353</td> <th>  Prob(JB):          </th> <td>   0.538</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.004</td> <th>  Cond. No.          </th> <td>    6.42</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                     产量   R-squared:                       0.825\n",
       "Model:                            OLS   Adj. R-squared:                  0.723\n",
       "Method:                 Least Squares   F-statistic:                     8.082\n",
       "Date:                Tue, 16 May 2023   Prob (F-statistic):           0.000955\n",
       "Time:                        11:06:20   Log-Likelihood:                -19.091\n",
       "No. Observations:                  20   AIC:                             54.18\n",
       "Df Residuals:                      12   BIC:                             62.15\n",
       "Df Model:                           7                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=================================================================================\n",
       "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------\n",
       "Intercept        11.7350      0.513     22.867      0.000      10.617      12.853\n",
       "C(品种)[T.2]        1.4000      0.574      2.440      0.031       0.150       2.650\n",
       "C(品种)[T.3]        1.8750      0.574      3.268      0.007       0.625       3.125\n",
       "C(品种)[T.4]        2.7750      0.574      4.836      0.000       1.525       4.025\n",
       "C(品种)[T.5]        2.4750      0.574      4.314      0.001       1.225       3.725\n",
       "施肥方案[T.施肥方案2]    -1.1800      0.513     -2.299      0.040      -2.298      -0.062\n",
       "施肥方案[T.施肥方案3]    -1.4800      0.513     -2.884      0.014      -2.598      -0.362\n",
       "施肥方案[T.施肥方案4]    -2.6800      0.513     -5.222      0.000      -3.798      -1.562\n",
       "==============================================================================\n",
       "Omnibus:                        1.845   Durbin-Watson:                   1.350\n",
       "Prob(Omnibus):                  0.398   Jarque-Bera (JB):                1.241\n",
       "Skew:                          -0.353   Prob(JB):                        0.538\n",
       "Kurtosis:                       2.004   Cond. No.                         6.42\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary() # 显示模型汇总信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "      <th>eta_sq_part</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(品种)</th>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0670</td>\n",
       "      <td>4.766750</td>\n",
       "      <td>7.239716</td>\n",
       "      <td>0.003315</td>\n",
       "      <td>0.707023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>施肥方案</th>\n",
       "      <td>3.0</td>\n",
       "      <td>18.1815</td>\n",
       "      <td>6.060500</td>\n",
       "      <td>9.204658</td>\n",
       "      <td>0.001949</td>\n",
       "      <td>0.697077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>12.0</td>\n",
       "      <td>7.9010</td>\n",
       "      <td>0.658417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            df   sum_sq   mean_sq         F    PR(>F)  eta_sq_part\n",
       "C(品种)      4.0  19.0670  4.766750  7.239716  0.003315     0.707023\n",
       "施肥方案       3.0  18.1815  6.060500  9.204658  0.001949     0.697077\n",
       "Residual  12.0   7.9010  0.658417       NaN       NaN          NaN"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (2) 计算偏效应量并进行分析。\n",
    "anova_table['eta_sq_part'] = anova_table['sum_sq'] / (anova_table['sum_sq'] + anova_table.loc['Residual', 'sum_sq']) # 计算偏效应量\n",
    "anova_table.loc['Residual', 'eta_sq_part'] = np.nan # 将残差的偏效应量设置为缺失值\n",
    "anova_table # 显示方差分析表"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "偏效应分析结果表明：\n",
    "- 在排除施肥方案的影响后，品种因子解释了产量误差的70.70%\n",
    "- 在排除品种的影响后，施肥方案因子解释了产量误差的69.71%"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>时段</th>\n",
       "      <th>路段1</th>\n",
       "      <th>路段2</th>\n",
       "      <th>路段3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>高峰期</td>\n",
       "      <td>36.5</td>\n",
       "      <td>28.1</td>\n",
       "      <td>32.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>高峰期</td>\n",
       "      <td>34.1</td>\n",
       "      <td>29.9</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>高峰期</td>\n",
       "      <td>37.2</td>\n",
       "      <td>32.2</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>高峰期</td>\n",
       "      <td>35.6</td>\n",
       "      <td>31.5</td>\n",
       "      <td>35.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>高峰期</td>\n",
       "      <td>38.0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>35.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>非高峰期</td>\n",
       "      <td>30.6</td>\n",
       "      <td>27.6</td>\n",
       "      <td>31.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>非高峰期</td>\n",
       "      <td>27.9</td>\n",
       "      <td>24.3</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>非高峰期</td>\n",
       "      <td>32.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>26.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>非高峰期</td>\n",
       "      <td>31.8</td>\n",
       "      <td>25.4</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>非高峰期</td>\n",
       "      <td>27.3</td>\n",
       "      <td>21.7</td>\n",
       "      <td>25.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     时段   路段1   路段2   路段3\n",
       "0   高峰期  36.5  28.1  32.4\n",
       "1   高峰期  34.1  29.9  33.0\n",
       "2   高峰期  37.2  32.2  36.2\n",
       "3   高峰期  35.6  31.5  35.5\n",
       "4   高峰期  38.0  30.1  35.1\n",
       "5  非高峰期  30.6  27.6  31.8\n",
       "6  非高峰期  27.9  24.3  28.0\n",
       "7  非高峰期  32.4  22.0  26.7\n",
       "8  非高峰期  31.8  25.4  29.3\n",
       "9  非高峰期  27.3  21.7  25.6"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./exercise/chap08/exercise8_5.csv', encoding='gbk') # 读取数据\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>时段</th>\n",
       "      <th>路段</th>\n",
       "      <th>行车时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>高峰期</td>\n",
       "      <td>路段1</td>\n",
       "      <td>36.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>高峰期</td>\n",
       "      <td>路段1</td>\n",
       "      <td>34.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>高峰期</td>\n",
       "      <td>路段1</td>\n",
       "      <td>37.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>高峰期</td>\n",
       "      <td>路段1</td>\n",
       "      <td>35.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>高峰期</td>\n",
       "      <td>路段1</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    时段   路段  行车时间\n",
       "0  高峰期  路段1  36.5\n",
       "1  高峰期  路段1  34.1\n",
       "2  高峰期  路段1  37.2\n",
       "3  高峰期  路段1  35.6\n",
       "4  高峰期  路段1  38.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (1) 分析路段、时段以及路段与时段交互作用对行车时间的影响（α=0.05）。\n",
    "df_melt = pd.melt(\n",
    "    df, id_vars=['时段'], value_vars=['路段1', '路段2', '路段3'],\n",
    "    value_name='行车时间', var_name='路段'\n",
    ") # 数据转换\n",
    "df_melt.head() # 显示前5行数据"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提出对于时段的假设：\n",
    "- $H_0: \\alpha_1 = \\alpha_2 = 0$(时段对行车时间的影响不显著)\n",
    "- $H_1: \\alpha_1, \\alpha_2$ 至少有一个不等于0(时段对行车时间的影响显著)\n",
    "\n",
    "提出对于路段的假设：\n",
    "- $H_0: \\beta_1 = \\beta_2 = \\beta_3 = 0$(路段对行车时间的影响不显著)\n",
    "- $H_1: \\beta_1, \\beta_2, \\beta_3$ 至少有一个不等于0(路段对行车时间的影响显著)\n",
    "\n",
    "提出对于时段与路段交互作用的假设：\n",
    "- $H_0: \\gamma_{ij} = 0, i = 1, 2, j = 1, 2, 3$(时段与路段交互作用对行车时间的影响不显著)\n",
    "- $H_1: \\gamma_{ij}$ 至少有一个不等于0(时段与路段交互作用对行车时间的影响显著) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>时段</th>\n",
       "      <td>1.0</td>\n",
       "      <td>288.300000</td>\n",
       "      <td>288.300000</td>\n",
       "      <td>70.331368</td>\n",
       "      <td>1.355779e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>路段</th>\n",
       "      <td>2.0</td>\n",
       "      <td>180.514667</td>\n",
       "      <td>90.257333</td>\n",
       "      <td>22.018459</td>\n",
       "      <td>3.711926e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>时段:路段</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.024000</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>0.002927</td>\n",
       "      <td>9.970772e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>24.0</td>\n",
       "      <td>98.380000</td>\n",
       "      <td>4.099167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            df      sum_sq     mean_sq          F        PR(>F)\n",
       "时段         1.0  288.300000  288.300000  70.331368  1.355779e-08\n",
       "路段         2.0  180.514667   90.257333  22.018459  3.711926e-06\n",
       "时段:路段      2.0    0.024000    0.012000   0.002927  9.970772e-01\n",
       "Residual  24.0   98.380000    4.099167        NaN           NaN"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ols('行车时间 ~ 时段 + 路段 + 时段:路段', data=df_melt).fit() # 拟合方差分析模型\n",
    "anova_table = anova_lm(model, typ=1) # 方差分析表\n",
    "anova_table # 显示方差分析表"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结果显示：\n",
    "- 对于时段、路段效应检验，P<0.05，所以拒绝原假设。\n",
    "- 对于交互作用效应检验，P>0.05，所以不能拒绝原假设。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>行车时间</td>       <th>  R-squared:         </th> <td>   0.827</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.790</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   22.87</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 16 May 2023</td> <th>  Prob (F-statistic):</th> <td>2.07e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:22:03</td>     <th>  Log-Likelihood:    </th> <td> -60.383</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    30</td>      <th>  AIC:               </th> <td>   132.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    24</td>      <th>  BIC:               </th> <td>   141.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>           <td>   30.0000</td> <td>    0.905</td> <td>   33.133</td> <td> 0.000</td> <td>   28.131</td> <td>   31.869</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>时段[T.高峰期]</th>           <td>    6.2800</td> <td>    1.280</td> <td>    4.904</td> <td> 0.000</td> <td>    3.637</td> <td>    8.923</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>路段[T.路段2]</th>           <td>   -5.8000</td> <td>    1.280</td> <td>   -4.529</td> <td> 0.000</td> <td>   -8.443</td> <td>   -3.157</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>路段[T.路段3]</th>           <td>   -1.7200</td> <td>    1.280</td> <td>   -1.343</td> <td> 0.192</td> <td>   -4.363</td> <td>    0.923</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>时段[T.高峰期]:路段[T.路段2]</th> <td>   -0.1200</td> <td>    1.811</td> <td>   -0.066</td> <td> 0.948</td> <td>   -3.857</td> <td>    3.617</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>时段[T.高峰期]:路段[T.路段3]</th> <td>   -0.1200</td> <td>    1.811</td> <td>   -0.066</td> <td> 0.948</td> <td>   -3.857</td> <td>    3.617</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.972</td> <th>  Durbin-Watson:     </th> <td>   1.985</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.226</td> <th>  Jarque-Bera (JB):  </th> <td>   1.391</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.078</td> <th>  Prob(JB):          </th> <td>   0.499</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 1.957</td> <th>  Cond. No.          </th> <td>    9.77</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   行车时间   R-squared:                       0.827\n",
       "Model:                            OLS   Adj. R-squared:                  0.790\n",
       "Method:                 Least Squares   F-statistic:                     22.87\n",
       "Date:                Tue, 16 May 2023   Prob (F-statistic):           2.07e-08\n",
       "Time:                        11:22:03   Log-Likelihood:                -60.383\n",
       "No. Observations:                  30   AIC:                             132.8\n",
       "Df Residuals:                      24   BIC:                             141.2\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=======================================================================================\n",
       "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------\n",
       "Intercept              30.0000      0.905     33.133      0.000      28.131      31.869\n",
       "时段[T.高峰期]               6.2800      1.280      4.904      0.000       3.637       8.923\n",
       "路段[T.路段2]              -5.8000      1.280     -4.529      0.000      -8.443      -3.157\n",
       "路段[T.路段3]              -1.7200      1.280     -1.343      0.192      -4.363       0.923\n",
       "时段[T.高峰期]:路段[T.路段2]    -0.1200      1.811     -0.066      0.948      -3.857       3.617\n",
       "时段[T.高峰期]:路段[T.路段3]    -0.1200      1.811     -0.066      0.948      -3.857       3.617\n",
       "==============================================================================\n",
       "Omnibus:                        2.972   Durbin-Watson:                   1.985\n",
       "Prob(Omnibus):                  0.226   Jarque-Bera (JB):                1.391\n",
       "Skew:                           0.078   Prob(JB):                        0.499\n",
       "Kurtosis:                       1.957   Cond. No.                         9.77\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary() # 显示模型汇总信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "      <th>eta_sq_part</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>时段</th>\n",
       "      <td>1.0</td>\n",
       "      <td>288.300000</td>\n",
       "      <td>288.300000</td>\n",
       "      <td>70.331368</td>\n",
       "      <td>1.355779e-08</td>\n",
       "      <td>0.745578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>路段</th>\n",
       "      <td>2.0</td>\n",
       "      <td>180.514667</td>\n",
       "      <td>90.257333</td>\n",
       "      <td>22.018459</td>\n",
       "      <td>3.711926e-06</td>\n",
       "      <td>0.647250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>时段:路段</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.024000</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>0.002927</td>\n",
       "      <td>9.970772e-01</td>\n",
       "      <td>0.000244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>24.0</td>\n",
       "      <td>98.380000</td>\n",
       "      <td>4.099167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            df      sum_sq     mean_sq          F        PR(>F)  eta_sq_part\n",
       "时段         1.0  288.300000  288.300000  70.331368  1.355779e-08     0.745578\n",
       "路段         2.0  180.514667   90.257333  22.018459  3.711926e-06     0.647250\n",
       "时段:路段      2.0    0.024000    0.012000   0.002927  9.970772e-01     0.000244\n",
       "Residual  24.0   98.380000    4.099167        NaN           NaN          NaN"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (2) 计算偏效应量并进行分析。\n",
    "anova_table['eta_sq_part'] = anova_table['sum_sq'] / (anova_table['sum_sq'] + anova_table.loc['Residual', 'sum_sq']) # 计算偏效应量\n",
    "anova_table.loc['Residual', 'eta_sq_part'] = np.nan # 将残差的偏效应量设置为缺失值\n",
    "anova_table # 显示方差分析表"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "偏效应分析结果表明：\n",
    "- 在排除路段和交互效应的影响后，时段因子解释了行车时间误差的74.56%\n",
    "- 在排除时段和交互效应的影响后，路段因子解释了行车时间误差的64.73%\n",
    "- 在排除时段和路段的影响后，交互效应因子解释了行车时间误差的0.024%"
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